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Roundtable Discussion:
Practical & Technical Approaches to Determining First-in-Human Doses

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Join us on Thursday, April 29 from 1:00pm – 2:30pm ET for a brief presentation followed by a roundtable panel discussion on “Practical & Technical Approaches to Determining First-in-Human Doses.”

Nuventra’s Celeste Vallejo, Ph.D., will kickstart the discussion by providing a brief overview to different nonclinical and modeling approaches for selecting First-in-Human (FIH) doses along with the benefits and drawbacks of each approach. The panelists include Nuventra’s Mark Bush, Ph.D., Teodora (Dora) Pene Dumitrescu, Ph.D., Mark Sale, M.D., & Salil Pendse M.S., who will be available to answer questions during the roundtable panel discussion.

The roundtable discussion will take a deeper look at various practical and technical considerations that need to be assessed before starting a FIH clinical trial. The panel will discuss costs, timelines, and data needs surrounding different types of approaches for selecting FIH doses and will provide some context on different ways to identify the appropriate method as it relates to larger drug development milestones.

At the end of this roundtable discussion attendees should have a better understanding of:

  • Costs and timelines associated with determining FIH doses
  • Model independent and model-based approaches to FIH doses
  • Dose selection for initial toxicology studies
  • Considerations for FIH doses depending on different routes of administration, molecule type, or indication

This event is free of charge.


Meet the Panelists & Moderator

Mark A. Bush, Ph.D.
Vice President, Clinical Pharmacology & Pharmacokinetics

Dr. Bush has over 20 years of experience in clinical pharmacology and pharmacokinetics with a particular focus in clinical pharmacology study design and interpretation. His therapeutic concentrations include endocrinology and metabolism in early phases of development. Dr. Bush has worked on hundreds of PK and popPK analyses throughout his career.

Teodora (Dora) Pene Dumitrescu, Ph.D.
Senior Consultant, Pharmacometrics

Dr. Dumitrescu has over 8 years of experience as a clinical pharmacology and pharmacometrics lead on cross-functional clinical development teams resulting in successful regulatory approvals. She has extensive experience in popPK/PD modeling, clinical trial simulation and design, probabilistic risk assessment, pediatric extrapolation, and applying quantitative pharmacology principles for decision making across multiple therapeutic areas.

Mark Sale, M.D.
Executive Vice President, Pharmacometrics

Dr. Sale has over 20 years of extensive experience conducting complex popPK analyses across diverse therapeutic areas and is one of the pharmaceutical industry’s thought leaders in modeling and simulation. Before joining Nuventra, Dr. Sale was the Global Director of Research Modeling and Simulation for GlaxoSmithKline prior to starting an independent consultancy in 2006 in popPK, modeling, and simulation.

Salil N. Pendse, M.S.
Quantitative Systems Pharmacologist II

Salil has more than 7 years of experience building mathematical models of cellular modes of action, physiologically-based pharmacokinetic (PBPK) modeling, bioinformatics, and building visualization tools for effective communication of complex scientific results. He has authored or co-authored more than 20 peer-reviewed publications in toxicology ranging from novel algorithms for interpreting transcriptomics data, to using PBPK modeling for informing chemical safety in juvenile populations.

Celeste Vallejo, Ph.D.
Quantitative Systems Pharmacologist I

Dr. Vallejo is experienced in model development in many different fields including those with differing data availability. She has designed various types of models used in understanding infectious disease transmission in which data can be scarce to neural networks in which data availability is much less restrained. Dr. Vallejo has applied numerous data analysis techniques in her work and developed an algorithm for sampling input parameters for a large-scale model in order to quantify model uncertainty.

 

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